Python for Machine Learning & Data Science Masterclass

About Course

Unlock the world of Data Science and Machine Learning with this comprehensive Python Masterclass, completely free on Theetay! This course, delivered by Jose Portilla and his team at Pierian Data Inc., equips you with the skills needed to thrive in this high-demand field.

This course is designed for learners with some Python experience who are ready to dive deeper into the world of data. You’ll master essential libraries like NumPy, Pandas, Matplotlib, and Seaborn, gaining a profound understanding of data analysis and visualization.

Furthermore, you’ll explore a wide range of machine learning algorithms using Scikit Learn, including:

Linear Regression
Regularization (Lasso, Ridge, Elastic Net)
K Nearest Neighbors
K Means Clustering
Decision Trees
Random Forests
Natural Language Processing
Support Vector Machines
Hierarchical Clustering
DBSCAN
PCA
Model Deployment

This course, originally on Udemy, offers a blend of practical real-world case studies and the mathematical theory behind machine learning algorithms, ensuring a deep understanding of both the “how” and the “why”. Join Theetay today and start your journey towards becoming a data science expert!

You will learn how to use data science and machine learning with Python.
You will create data pipeline workflows to analyze, visualize, and gain insights from data.
You will build a portfolio of data science projects with real world data.
You will be able to analyze your own data sets and gain insights through data science.
Master critical data science skills.
Understand Machine Learning from top to bottom.
Replicate real-world situations and data reports.
Learn NumPy for numerical processing with Python.
Conduct feature engineering on real world case studies.
Learn Pandas for data manipulation with Python.
Create supervised machine learning algorithms to predict classes.
Learn Matplotlib to create fully customized data visualizations with Python.
Create regression machine learning algorithms for predicting continuous values.
Learn Seaborn to create beautiful statistical plots with Python.
Construct a modern portfolio of data science and machine learning resume projects.
Learn how to use Scikit-learn to apply powerful machine learning algorithms.
Get set-up quickly with the Anaconda data science stack environment.
Learn best practices for real-world data sets.
Understand the full product workflow for the machine learning lifecycle.
Explore how to deploy your machine learning models as interactive APIs.

Course Content

01 – Introduction to Course

A Message from the Professor
001 Welcome to the Course_.html

00:00
002 COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP_.mp4

00:00
003 Anaconda Python and Jupyter Install and Setup.mp4

00:00
004 Note on Environment Setup – Please read me_.html

00:00
005 Environment Setup.mp4

00:00
28813464-requirements.txt

00:00
33985574-UNZIP-FOR-NOTEBOOKS-FINAL.zip

00:00
external-assets-links.txt

00:00
Section Quiz

02 – OPTIONAL_ Python Crash Course

03 – Machine Learning Pathway Overview

04 – NumPy

05 – Pandas

001 Introduction to Pandas.mp4

00:00
002 Series – Part One.mp4

00:00
003 Series – Part Two.mp4

00:00
004 DataFrames – Part One – Creating a DataFrame.mp4

00:00
005 DataFrames – Part Two – Basic Properties.mp4

00:00
006 DataFrames – Part Three – Working with Columns.mp4

00:00
007 DataFrames – Part Four – Working with Rows.mp4

00:00
008 Pandas – Conditional Filtering.mp4

00:00
009 Pandas – Useful Methods – Apply on Single Column.mp4

00:00
010 Pandas – Useful Methods – Apply on Multiple Columns.mp4

00:00
011 Pandas – Useful Methods – Statistical Information and Sorting.mp4

00:00
012 Missing Data – Overview.mp4

00:00
013 Missing Data – Pandas Operations.mp4

00:00
014 GroupBy Operations – Part One.mp4

00:00
015 GroupBy Operations – Part Two – MultiIndex.mp4

00:00
016 Combining DataFrames – Concatenation.mp4

00:00
017 Combining DataFrames – Inner Merge.mp4

00:00
018 Combining DataFrames – Left and Right Merge.mp4

00:00
019 Combining DataFrames – Outer Merge.mp4

00:00
020 Pandas – Text Methods for String Data.mp4

00:00
021 Pandas – Time Methods for Date and Time Data.mp4

00:00
022 Pandas Input and Output – CSV Files.mp4

00:00
023 Pandas Input and Output – HTML Tables.mp4

00:00
024 Pandas Input and Output – Excel Files.mp4

00:00
025 Pandas Input and Output – SQL Databases.mp4

00:00
026 Pandas Pivot Tables.mp4

00:00
027 Pandas Project Exercise Overview.mp4

00:00
028 Pandas Project Exercise Solutions.mp4

00:00
Section Quiz

06 – Matplotlib

001 Introduction to Matplotlib.mp4

00:00
002 Matplotlib Basics.mp4

00:00
003 Matplotlib – Understanding the Figure Object.mp4

00:00
004 Matplotlib – Implementing Figures and Axes.mp4

00:00
005 Matplotlib – Figure Parameters.mp4

00:00
006 Matplotlib – Subplots Functionality.mp4

00:00
007 Matplotlib Styling – Legends.mp4

00:00
008 Matplotlib Styling – Colors and Styles.mp4

00:00
009 Advanced Matplotlib Commands (Optional).mp4

00:00
010 Matplotlib Exercise Questions Overview.mp4

00:00
011 Matplotlib Exercise Questions – Solutions.mp4

00:00
Section Quiz

07 – Seaborn Data Visualizations

001 Introduction to Seaborn.mp4

00:00
002 Scatterplots with Seaborn.mp4

00:00
003 Distribution Plots – Part One – Understanding Plot Types.mp4

00:00
004 Distribution Plots – Part Two – Coding with Seaborn.mp4

00:00
005 Categorical Plots – Statistics within Categories – Understanding Plot Types.mp4

00:00
006 Categorical Plots – Statistics within Categories – Coding with Seaborn.mp4

00:00
007 Categorical Plots – Distributions within Categories – Understanding Plot Types.mp4

00:00
008 Categorical Plots – Distributions within Categories – Coding with Seaborn.mp4

00:00
009 Seaborn – Comparison Plots – Understanding the Plot Types.mp4

00:00
010 Seaborn – Comparison Plots – Coding with Seaborn.mp4

00:00
011 Seaborn Grid Plots.mp4

00:00
012 Seaborn – Matrix Plots.mp4

00:00
013 Seaborn Plot Exercises Overview.mp4

00:00
014 Seaborn Plot Exercises Solutions.mp4

00:00
Section Quiz

08 – Data Analysis and Visualization Capstone Project Exercise

09 – Machine Learning Concepts Overview

10 – Linear Regression

001 Introduction to Linear Regression Section.mp4

00:00
002 Linear Regression – Algorithm History.mp4

00:00
003 Linear Regression – Understanding Ordinary Least Squares.mp4

00:00
004 Linear Regression – Cost Functions.mp4

00:00
005 Linear Regression – Gradient Descent.mp4

00:00
006 Python coding Simple Linear Regression.mp4

00:00
007 Overview of Scikit-Learn and Python.mp4

00:00
008 Linear Regression – Scikit-Learn Train Test Split.mp4

00:00
009 Linear Regression – Scikit-Learn Performance Evaluation – Regression.mp4

00:00
010 Linear Regression – Residual Plots.mp4

00:00
011 Linear Regression – Model Deployment and Coefficient Interpretation.mp4

00:00
012 Polynomial Regression – Theory and Motivation.mp4

00:00
013 Polynomial Regression – Creating Polynomial Features.mp4

00:00
014 Polynomial Regression – Training and Evaluation.mp4

00:00
015 Bias Variance Trade-Off.mp4

00:00
016 Polynomial Regression – Choosing Degree of Polynomial.mp4

00:00
017 Polynomial Regression – Model Deployment.mp4

00:00
018 Regularization Overview.mp4

00:00
019 Feature Scaling.mp4

00:00
020 Introduction to Cross Validation.mp4

00:00
021 Regularization Data Setup.mp4

00:00
022 L2 Regularization – Ridge Regression Theory.mp4

00:00
023 L2 Regularization – Ridge Regression – Python Implementation.mp4

00:00
024 L1 Regularization – Lasso Regression – Background and Implementation.mp4

00:00
025 L1 and L2 Regularization – Elastic Net.mp4

00:00
026 Linear Regression Project – Data Overview.mp4

00:00

11 – Feature Engineering and Data Preparation

001 A note from Jose on Feature Engineering and Data Preparation.html

00:00
002 Introduction to Feature Engineering and Data Preparation.mp4

00:00
003 Dealing with Outliers.mp4

00:00
004 Dealing with Missing Data _ Part One – Evaluation of Missing Data.mp4

00:00
005 Dealing with Missing Data _ Part Two – Filling or Dropping data based on Rows.mp4

00:00
006 Dealing with Missing Data _ Part 3 – Fixing data based on Columns.mp4

00:00
007 Dealing with Categorical Data – Encoding Options.mp4

00:00
Section Quiz

12 – Cross Validation , Grid Search, and the Linear Regression Project

001 Section Overview and Introduction.mp4

00:00
002 Cross Validation – Test _ Train Split.mp4

00:00
003 Cross Validation – Test _ Validation _ Train Split.mp4

00:00
004 Cross Validation – cross_val_score.mp4

00:00
005 Cross Validation – cross_validate.mp4

00:00
006 Grid Search.mp4

00:00
007 Linear Regression Project Overview.mp4

00:00
008 Linear Regression Project – Solutions.mp4

00:00
Section Quiz

13 – Logistic Regression

001 Early Bird Note on Downloading .zip for Logistic Regression Notes.html

00:00
002 Introduction to Logistic Regression Section.mp4

00:00
003 Logistic Regression – Theory and Intuition – Part One_ The Logistic Function.mp4

00:00
004 Logistic Regression – Theory and Intuition – Part Two_ Linear to Logistic.mp4

00:00
005 Logistic Regression – Theory and Intuition – Linear to Logistic Math.mp4

00:00
006 Logistic Regression – Theory and Intuition – Best fit with Maximum Likelihood.mp4

00:00
007 Logistic Regression with Scikit-Learn – Part One – EDA.mp4

00:00
008 Logistic Regression with Scikit-Learn – Part Two – Model Training.mp4

00:00
009 Classification Metrics – Confusion Matrix and Accuracy.mp4

00:00
010 Classification Metrics – Precison, Recall, F1-Score.mp4

00:00
011 Classification Metrics – ROC Curves.mp4

00:00
012 Logistic Regression with Scikit-Learn – Part Three – Performance Evaluation.mp4

00:00
013 Multi-Class Classification with Logistic Regression – Part One – Data and EDA.mp4

00:00
014 Multi-Class Classification with Logistic Regression – Part Two – Model.mp4

00:00
015 Logistic Regression Exercise Project Overview.mp4

00:00
016 Logistic Regression Project Exercise – Solutions.mp4

00:00
29304858-11-Logistic-Regression-Models.zip

00:00
Section Quiz

14 – KNN – K Nearest Neighbors

15 – Support Vector Machines

001 Introduction to Support Vector Machines.mp4

00:00
002 History of Support Vector Machines.mp4

00:00
003 SVM – Theory and Intuition – Hyperplanes and Margins.mp4

00:00
004 SVM – Theory and Intuition – Kernel Intuition.mp4

00:00
005 SVM – Theory and Intuition – Kernel Trick and Mathematics.mp4

00:00
006 SVM with Scikit-Learn and Python – Classification Part One.mp4

00:00
007 SVM with Scikit-Learn and Python – Classification Part Two.mp4

00:00
008 SVM with Scikit-Learn and Python – Regression Tasks.mp4

00:00
009 Support Vector Machine Project Overview.mp4

00:00
010 Support Vector Machine Project Solutions.mp4

00:00
29902052-13-Support-Vector-Machines.zip

00:00
Section Quiz

16 – Tree Based Methods_ Decision Tree Learning

001 Introduction to Tree Based Methods.mp4

00:00
002 Decision Tree – History.mp4

00:00
003 Decision Tree – Terminology.mp4

00:00
004 Decision Tree – Understanding Gini Impurity.mp4

00:00
005 Constructing Decision Trees with Gini Impurity – Part One.mp4

00:00
006 Constructing Decision Trees with Gini Impurity – Part Two.mp4

00:00
007 Coding Decision Trees – Part One – The Data.mp4

00:00
008 Coding Decision Trees – Part Two -Creating the Model.mp4

00:00
30205020-14-Decision-Trees.zip

00:00
Section Quiz

17 – Random Forests

001 Introduction to Random Forests Section.mp4

00:00
002 Random Forests – History and Motivation.mp4

00:00
003 Random Forests – Key Hyperparameters.mp4

00:00
004 Random Forests – Number of Estimators and Features in Subsets.mp4

00:00
005 Random Forests – Bootstrapping and Out-of-Bag Error.mp4

00:00
006 Coding Classification with Random Forest Classifier – Part One.mp4

00:00
007 Coding Classification with Random Forest Classifier – Part Two.mp4

00:00
008 Coding Regression with Random Forest Regressor – Part One – Data.mp4

00:00
009 Coding Regression with Random Forest Regressor – Part Two – Basic Models.mp4

00:00
010 Coding Regression with Random Forest Regressor – Part Three – Polynomials.mp4

00:00
011 Coding Regression with Random Forest Regressor – Part Four – Advanced Models.mp4

00:00
30930956-15-Random-Forests.zip

00:00
Section Quiz

18 – Boosting Methods

001 Introduction to Boosting Section.mp4

00:00
002 Boosting Methods – Motivation and History.mp4

00:00
003 AdaBoost Theory and Intuition.mp4

00:00
004 AdaBoost Coding Part One – The Data.mp4

00:00
005 AdaBoost Coding Part Two – The Model.mp4

00:00
006 Gradient Boosting Theory.mp4

00:00
007 Gradient Boosting Coding Walkthrough.mp4

00:00
31286608-16-Boosted-Trees.zip

00:00
Section Quiz

19 – Supervised Learning Capstone Project – Cohort Analysis and Tree Based Methods

20 – Naive Bayes Classification and Natural Language Processing (Supervised Learning)

001 Introduction to NLP and Naive Bayes Section.mp4

00:00
002 Naive Bayes Algorithm – Part One – Bayes Theorem.mp4

00:00
003 Naive Bayes Algorithm – Part Two – Model Algorithm.mp4

00:00
004 Feature Extraction from Text – Part One – Theory and Intuition.mp4

00:00
005 Feature Extraction from Text – Coding Count Vectorization Manually.mp4

00:00
006 Feature Extraction from Text – Coding with Scikit-Learn.mp4

00:00
007 Natural Language Processing – Classification of Text – Part One.mp4

00:00
008 Natural Language Processing – Classification of Text – Part Two.mp4

00:00
009 Text Classification Project Exercise Overview.mp4

00:00
010 Text Classification Project Exercise Solutions.mp4

00:00
31640094-18-Naive-Bayes-and-NLP.zip

00:00
Section Quiz

21 – Unsupervised Learning

22 – K-Means Clustering

001 Introduction to K-Means Clustering Section.mp4

00:00
002 Clustering General Overview.mp4

00:00
003 K-Means Clustering Theory.mp4

00:00
004 K-Means Clustering – Coding Part One.mp4

00:00
005 K-Means Clustering Coding Part Two.mp4

00:00
006 K-Means Clustering Coding Part Three.mp4

00:00
007 K-Means Color Quantization – Part One.mp4

00:00
008 K-Means Color Quantization – Part Two.mp4

00:00
009 K-Means Clustering Exercise Overview.mp4

00:00
010 K-Means Clustering Exercise Solution – Part One.mp4

00:00
011 K-Means Clustering Exercise Solution – Part Two.mp4

00:00
012 K-Means Clustering Exercise Solution – Part Three.mp4

00:00
32407448-20-Kmeans-Clustering.zip

00:00
33555798-palm-trees.jpg

00:00
Section Quiz

23 – Hierarchical Clustering

24 – DBSCAN – Density-based spatial clustering of applications with noise

001 Introduction to DBSCAN Section.mp4

00:00
002 DBSCAN – Theory and Intuition.mp4

00:00
003 DBSCAN versus K-Means Clustering.mp4

00:00
004 DBSCAN – Hyperparameter Theory.mp4

00:00
005 DBSCAN – Hyperparameter Tuning Methods.mp4

00:00
006 DBSCAN – Outlier Project Exercise Overview.mp4

00:00
007 DBSCAN – Outlier Project Exercise Solutions.mp4

00:00
33643014-22-DBSCAN.zip

00:00
external-assets-links.txt

00:00
Section Quiz

25 – PCA – Principal Component Analysis and Manifold Learning

001 Introduction to Principal Component Analysis.mp4

00:00
002 PCA Theory and Intuition – Part One.mp4

00:00
003 PCA Theory and Intuition – Part Two.mp4

00:00
004 PCA – Manual Implementation in Python.mp4

00:00
005 PCA – SciKit-Learn.mp4

00:00
006 PCA – Project Exercise Overview.mp4

00:00
007 PCA – Project Exercise Solution.mp4

00:00
33912220-23-PCA-Principal-Component-Analysis.zip

00:00
Section Quiz

26 – Model Deployment

Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

Student Ratings & Reviews

No Review Yet

About Course

What Will You Learn?

Course Content

01 – Introduction to Course

A Message from the Professor

001 Welcome to the Course_.html

002 COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP_.mp4

003 Anaconda Python and Jupyter Install and Setup.mp4

004 Note on Environment Setup – Please read me_.html

005 Environment Setup.mp4

28813464-requirements.txt

33985574-UNZIP-FOR-NOTEBOOKS-FINAL.zip

external-assets-links.txt

Section Quiz

02 – OPTIONAL_ Python Crash Course

001 OPTIONAL_ Python Crash Course.html

002 Python Crash Course – Part One.mp4

003 Python Crash Course – Part Two.mp4

004 Python Crash Course – Part Three.mp4

005 Python Crash Course – Exercise Questions.mp4

006 Python Crash Course – Exercise Solutions.mp4

Section Quiz

03 – Machine Learning Pathway Overview

001 Machine Learning Pathway.mp4

Section Quiz

04 – NumPy

001 Introduction to NumPy.mp4

002 NumPy Arrays.mp4

003 NumPy Indexing and Selection.mp4

004 NumPy Operations.mp4

005 NumPy Exercises.mp4

006 Numpy Exercises – Solutions.mp4

Section Quiz

05 – Pandas

001 Introduction to Pandas.mp4

002 Series – Part One.mp4

003 Series – Part Two.mp4

004 DataFrames – Part One – Creating a DataFrame.mp4

005 DataFrames – Part Two – Basic Properties.mp4

006 DataFrames – Part Three – Working with Columns.mp4

007 DataFrames – Part Four – Working with Rows.mp4

008 Pandas – Conditional Filtering.mp4

009 Pandas – Useful Methods – Apply on Single Column.mp4

010 Pandas – Useful Methods – Apply on Multiple Columns.mp4

011 Pandas – Useful Methods – Statistical Information and Sorting.mp4

012 Missing Data – Overview.mp4

013 Missing Data – Pandas Operations.mp4

014 GroupBy Operations – Part One.mp4

015 GroupBy Operations – Part Two – MultiIndex.mp4

016 Combining DataFrames – Concatenation.mp4

017 Combining DataFrames – Inner Merge.mp4

018 Combining DataFrames – Left and Right Merge.mp4

019 Combining DataFrames – Outer Merge.mp4

020 Pandas – Text Methods for String Data.mp4

021 Pandas – Time Methods for Date and Time Data.mp4

022 Pandas Input and Output – CSV Files.mp4

023 Pandas Input and Output – HTML Tables.mp4

024 Pandas Input and Output – Excel Files.mp4

025 Pandas Input and Output – SQL Databases.mp4

026 Pandas Pivot Tables.mp4

027 Pandas Project Exercise Overview.mp4

028 Pandas Project Exercise Solutions.mp4

Section Quiz

06 – Matplotlib

001 Introduction to Matplotlib.mp4

002 Matplotlib Basics.mp4

003 Matplotlib – Understanding the Figure Object.mp4

004 Matplotlib – Implementing Figures and Axes.mp4

005 Matplotlib – Figure Parameters.mp4

006 Matplotlib – Subplots Functionality.mp4

007 Matplotlib Styling – Legends.mp4

008 Matplotlib Styling – Colors and Styles.mp4

009 Advanced Matplotlib Commands (Optional).mp4

010 Matplotlib Exercise Questions Overview.mp4

011 Matplotlib Exercise Questions – Solutions.mp4

Section Quiz

07 – Seaborn Data Visualizations

001 Introduction to Seaborn.mp4

002 Scatterplots with Seaborn.mp4

003 Distribution Plots – Part One – Understanding Plot Types.mp4